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Transcript of Peter Van Overschee & Christiaan Moons Slide 1 Creators in control Technologielaan 11/0101, B-3001...
Peter Van Overschee & Christiaan Moons
Slide 1
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
Industrial Challenges for
the Identification and Control
Society
Peter Van Overschee & Christiaan Moons
Slide 2
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
Background ISMC (Spin Off KU Leuven)
+IPCOS Technology (TU Eindhoven / TU Delft)
• Advanced Process Control (APC) products
• All affiliated services: consulting, feasibility studies, implementation, training, maintenance
Power Production
Chemical Processing &
refining
Glass Manufacturing
OilProduction
Peter Van Overschee & Christiaan Moons
Slide 3
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
Academic versus Industrial• Academic research is mathemically / problem driven
– How challenging is the APC problem ?– How do I make an as good as possible model ?
• Industrial Advanced Process Control (APC) applications are economically driven – How can I make money by solving the APC problem ? – How do I make a good enough model as cheap as possible ?
Typical Payback of a good APC project must lie within 3-12 months
Industrial Challenges are always economically driven
Peter Van Overschee & Christiaan Moons
Slide 4
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
Presentation Structure
•Plant Operation Layers•Typical Advanced Process Control applications
Low Level Control Tuning Soft Sensors Multivariable/Predictive Control Plant-Wide Dynamic Optimization
Presentation Goal•Understand Principles of each layer •Understand Economics of each layer •Discuss Academic Challenges
Peter Van Overschee & Christiaan Moons
Slide 5
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
Plant
Operation Layers
Peter Van Overschee & Christiaan Moons
Slide 6
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
Plant Operation Layers
Process Plant Operation is layered
1. Low Level Control (PID)
2. Supervisory Control (Softsensors & MPC)
3. Plantwide Optimization (Optimisation)
At each layer other technologies & timescales apply and different benefits result
Peter Van Overschee & Christiaan Moons
Slide 7
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
Model Based Control & Optimization
Technology Layer
Dynamic model based trajectory optimisation
Plant-Wide Optimisation
Optimal Primary PID Controllers
Low Level Control
ProcessProcess
Process
Model Predictive
Control
Optimal Reference Signals
Model Predictive
Control
Model Predictive
Control
Plant-Wide Model Based
Optimizer
Optimal Process Conditions
DCSDCS
DCS
Primary Control Signals
Control hierarchy
High Performance MPCTrajectory tracking MPC
Supervisory Control
Soft Sensors
Peter Van Overschee & Christiaan Moons
Slide 8
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
1. Low Level Control Layer
Control: PID, On-Off… Platform: DCS, PLC…Timescale: secondsBenefits: stability
FIC
FIC
FICCW
SplitFIC
FIC
Ratio Station
TIC T-Setpoint
PICp-Setpoint
LIC
L-Setpoint
Peter Van Overschee & Christiaan Moons
Slide 9
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
2. Supervisory Control Layer
Steam Flow Setpoint for low level control
Multivariable Controller
Concentration SetpointTopConcentration SetpointBottom
Ratio Station
FIC
Ratio Setpoint for low level control
Control: MPC,… Platform: PC, DCSTimescale: seconds - minutesBenefits: operate closer to
constraints
Peter Van Overschee & Christiaan Moons
Slide 10
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]. Plantwide Optimization
Layer
E
D
A
BC
E
D
A
BC
Plantwide Optimization : 2 types
Static Optimizer : Detects steady stateFind optimal steady stateHands optimal setpoints down to Supervisory control layer
Dynamic Optimizer: Find Optimal Dynamic Trajectories
Platform: PCTimescale: hours-daysBenefits: economical optimization
(plant constraints)
Peter Van Overschee & Christiaan Moons
Slide 11
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
Principles
Economics &
Challenges
Peter Van Overschee & Christiaan Moons
Slide 12
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected] Based Control &
OptimizationPID Controllers
Technology Product
Dynamic model based trajectory optimisation
PathFinder
High Performance MPCTrajectory tracking MPC
INCA
Soft Sensors Presto
Optimal Primary PID Controllers
RaPID
ProcessProcess
Process
Model Predictive
Control
Optimal Reference Signals
Model Predictive
Control
Model Predictive
Control
Plant-Wide Model Based
Optimizer
Optimal Process Conditions
DCSDCS
DCS
Primary Control Signals
Control hierarchy
Peter Van Overschee & Christiaan Moons
Slide 13
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
PID Controller Principles
R(s) Y(s)U(s)E(s) System P(s)
Controller
Proportional Part
Integral Part
Derivative Part
dt
tdedTdtte
TteKtu d
t
ip
)()(
1)()(
0
Peter Van Overschee & Christiaan Moons
Slide 14
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
PID Controller EconomicsProcess Performance is not as good as you think
• PID controllers at lowest level
• PID controllers are the “workhorse” of Process Industry
• 90 % of the controllers are PID’s
• More than 30 % of PID’s operates in manual
• More than 30 % of loops increase short term variability
• About 25 % of loops use default settings
• About 30 % of loops have equipment problems
• APC not useful when PID’s are badly tuned
Peter Van Overschee & Christiaan Moons
Slide 15
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected] Controller
Economics Chemical example
A B
Cooling water
Steam
• Where: Antwerp• Company: Confidential • Product: Confidential • Solution: optimal PID control for batch • Benefit: 1.000.000 €/year/reactor• Payback: 3 weeks• How was the benefit generated:
• Batch time reduction through increasedthroughput in a non saturated market
Peter Van Overschee & Christiaan Moons
Slide 16
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
PID Controller Economics Refining Example
• Where: Antwerp (Belgium)• Company: BRC• Product: refining • Solution: Optimization of primary loops (ES)• How was the benefit generated:
• More stable operation (operator load)• Less blending • Less system load (lifetime)• First step towards APC
Peter Van Overschee & Christiaan Moons
Slide 17
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
PID Controller Challenges (1) Industrial Requirements
Y(s)U(s)E(s)R(s) Controller C(s)
System P(s)
LoadL(s)
Disturbance D(s)
Industrial Requirements• Good Load Rejection• No nervous control signal• No overshoot• Fast Tracking (for reference or master/slave controllers)• Robust
Peter Van Overschee & Christiaan Moons
Slide 18
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
PID Controller challenges (2)
• Often academically “miss-treated” because of apparent simplicity
• Main Challenge lies in the trade off that must be made between performance and robustness with a limited PID control structure
• Operators have to tune & maintain PID Controllers: Automate tuning as much as possible
• Use operational (closed loop) data
• Simple operational requirements
• Simple trade off tracking and load rejection requirements
• Auto detection of need for re-tuning
• Fast i.e. within ¼ day
Peter Van Overschee & Christiaan Moons
Slide 19
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
PID Controller Challenges (3)
Identification Society
• Fully automatic identification of SISO dynamic systems including estimation of delay, #poles, #zeros,
integrator…
• From badly excited, closed-loop data with low frequent disturbances
• Leading to physically acceptable models
Peter Van Overschee & Christiaan Moons
Slide 20
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
PID Controller Challenges (4)
Control Society
• Simple, engineering based statements of the PID control problem with operational constraints (MV saturation)
• Good and fast optimisation strategies
• Keeping the industrial form of the PID controllers in mind
• Pairing of MV to CV
• PID structures & paradigms (cascade, split range)
• Automatic detection of troublemakers within x00 PID’s
Peter Van Overschee & Christiaan Moons
Slide 21
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected] Based Control &
OptimizationSoft Sensors
Technology Product
Dynamic model based trajectory optimisation
PathFinder
High Performance MPCTrajectory tracking MPC
INCA
Soft Sensors Presto
Optimal Primary PID Controllers
RaPID
ProcessProcess
Process
Model Predictive
Control
Optimal Reference Signals
Model Predictive
Control
Model Predictive
Control
Plant-Wide Model Based
OptimizerOptimal Process Conditions
DCSDCS
DCS
Primary Control Signals
Control hierarchy
Peter Van Overschee & Christiaan Moons
Slide 22
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
Soft Sensor Principles
Classical
Concentrations,
Density, MI,
pH, NOx, CO2
€€€€€
Confidence level
On-line
Flows, Pressures, Temperatures
Soft Sensor
Peter Van Overschee & Christiaan Moons
Slide 23
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
Soft Sensor Economics
• Avoid using expensive measurement equipment
• Less use of laboratory
• Closed Loop (High bandwidth)
• Possible to put derived process variables (e.g. Efficiency, Emissions) in control loops
More competitive operation
Better environmental protection
• Predict possible future problems (pumps, fans, valves)
Less emergency stops
Maintenance can be planned better; predictive maintenance
Peter Van Overschee & Christiaan Moons
Slide 24
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
Soft Sensor Economics Oil industry example
• Where: Asia• Company: Confidential • Product: Oil • Solution: On-line estimation of multi-phase flows • Benefit: x.000.000 €/year/platform• Payback: weeks• How was the benefit generated:
• Continuous soft-measurement allows for on-line monitoring and optimization
Peter Van Overschee & Christiaan Moons
Slide 25
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
Soft Sensor Challenges (1)
Identification Society
1) Input Selection: How to select a subset of inputs (10) for the model from the huge set of available inputs (100)
• Need heuristics to avoid computation for years by exhaustive search
• PCA, PLS, CCA only determine a linear combination
• Avoid over-fitting in the input space
• Huge data sets (1 Mio samples x 300 variables)
Peter Van Overschee & Christiaan Moons
Slide 26
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
Soft Sensor Challenges (2)
2) Modelling: How to make accurate models from the data
• Structure: Static and dynamic, linear and non-linear
• Huge amount of data (1 Mio x 300): computationally efficient
• Good initial guesses for optimisation
• Highly correlated historical (closed loop) data
• Automatic trade-off between accuracy and generality (overfitting)
0 200 400 600 800 1000 1200 14000.6
0.8
1
1.2
1.4
1.6
1.8
2
2.2
2.4
2.6
Training Steps
Err
or
Validation ErrorTraining Error
Peter Van Overschee & Christiaan Moons
Slide 27
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
Soft Sensor Challenges (3)
3) Make accuracy of models depending on local input densities
4) Allow “reasonable” extrapolation of Soft Sensors through a-priori knowledge.
Controller
*
Much Data Few Data Much Data
*** *
**
**
**
** *
***
Peter Van Overschee & Christiaan Moons
Slide 28
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
Soft Sensor Challenges (4)
5) On-line updating based on new information
• Bias correction, Kalman Filter or Receding Horizon Estimator
• As long as it is robust and easy to use
• And cheap
6) On-line: Track accuracy of model and flag when model leaves training region and extrapolates excessively
Peter Van Overschee & Christiaan Moons
Slide 29
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
Model Based Control & Optimization
Model Based Predictive ControlTechnology Product
Dynamic model based trajectory optimisation
PathFinder
High Performance MPCTrajectory tracking MPC
INCA
Soft Sensors Presto
Optimal Primary PID Controllers
RaPID
ProcessProcess
Process
Model Predictive
Control
Optimal Reference Signals
Model Predictive
Control
Model Predictive
Control
Plant-Wide Model Based
OptimizerOptimal Process Conditions
DCSDCS
DCS
Primary Control Signals
Control hierarchy
Peter Van Overschee & Christiaan Moons
Slide 30
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
Principles
Y1(t)
U1(t)
Y1(t)U1(t)PLANT
Y2(t)U2(t)
^
^
Y1(t)U1(t)Model
Y2(t)U2(t)
^
^
U2(t)
Safety
Disch. Pres. Air compr
Feed CH4
T exit Prim Reformer
Gas composition change
Gas composition Change (DISTURBANCE)
Y2(t)%CH4 (sec ref.)
Quality
Throughput
Peter Van Overschee & Christiaan Moons
Slide 31
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
MPC Economics: Chemical Example
•Where: Burgkirchen-Gendorf (Germany)• Company: Vinnolit • Product: Vinylchlorid• Solution: APC (Products and ES)• Benefit: confidential• How the benefit was generated generated:
• Energy• Throughput• Reduced maintenance cost
P
Fuel gas
FeedEDC
EDC / VC / HCl
CrackingFurnace
evaporatorsuperheater
waste gas
T
P
L
TF
H
F
condenser
Peter Van Overschee & Christiaan Moons
Slide 32
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
time
100%
97%
Actuator Value
= extra throughput
APC on
How?Higher throughput
APC off
MPC Economics Variance Reduction
Peter Van Overschee & Christiaan Moons
Slide 33
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
time
Quality
= Profit
APC off
APC onAPC off
How?Reduced Energy/cost
Peter Van Overschee & Christiaan Moons
Slide 34
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
time
Process Value
Specification A
Specification B
Ideal Value
Ideal Value
transitionstart
= Profit
controlledtransition
manualtransition
transitionend
transitionend
How?Controlled Transitions in Automatic
Mode
Peter Van Overschee & Christiaan Moons
Slide 35
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
MPC Economics on an EDC/VC cracker
Peter Van Overschee & Christiaan Moons
Slide 36
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
0
0.51
1.52
2.53
prob
abili
ty d
ensi
ty fu
nctio
n
probability density
Cpk
= 0
.96
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
x 104
19
19.2
19.4
19.6
19.8
20
20.2
20.4
20.6
20.8
21Measured process signal
time
valu
e
Visualization of benefit realization by MPC
0246810
12
Cpk
= 0
.96
Cpk
= 4
.3
0246810
12
Cpk
= 0
.96
Cpk
= 1
.6
Economicbenefit
Standard ControlStandard ControlModel Model Predictive Predictive Control Control without without optimizationoptimization
Model Model Predictive Predictive Control with Control with performance performance optimizationoptimization
MPC Economics Variance Reduction
Peter Van Overschee & Christiaan Moons
Slide 37
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
MPC Challenges (1) Identification
• Need accurate multivariable dynamic models
• With a minimal test time – Multivariable Models are Expensive
• Up to 40 % of an MPC project costs
• Excite multiple input variables at the same time
• Avoid waiting for the process to settle (settling times of 24 hours and more)
• Insensitive for low frequent disturbances
• Insensitive for (de-tuned) controller in the loop
• Carefully designed experiments (cfr. stiff systems)
Peter Van Overschee & Christiaan Moons
Slide 38
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
MPC Challenges: Testing
Model
€€ €€ €€ €€
MODEL
€€
Peter Van Overschee & Christiaan Moons
Slide 39
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
MPC Challenges: Use of Step Response Model
NNNN u
u
u
u
sss
sss
ss
s
Y
y
y
y
y
2
1
0
01
012
01
0
02
1
0
0
0
00
Linear Relationship: Y = F + G U• Holds also for Multiple Input Multiple Output system case• Easy model building• Low performance (high frequency content)• Long testing time
U Ys0
s1
s2
Peter Van Overschee & Christiaan Moons
Slide 40
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected] Challenges:
Use of State Space Models xk+1 = A xk + B uk
yk = C xk + D uk
N
NNNN u
u
u
u
DBCABCA
DCBCAB
DCB
D
x
CA
CA
CA
C
y
y
y
y
2
1
0
21
02
2
1
0
0
0
00
y0 = Cx0 + Du0
y1 = CAx0 + CB u0
y2 = CA2x0 + CABu0 + CBu1 + Du2 …
Linear Relationship: Y = F + G U
• Holds also for Multiple Input Multiple Output system case• Easy adaptation for Linear Time Variant model (Ak,Bk,Ck,Dk)• Easy Identification from test data or from rigorous process model• Stiff systems
Peter Van Overschee & Christiaan Moons
Slide 41
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
MPC Challenges (2)
y(t)+
+
first principlessimulation
model
process
Low pass filter H1(s)
High pass filter H2(s)
u(t)
First principle model use for identification
Re-use as much a-prior knowledge as possible by using First Principle Models - Multivariable Models are Expensive
• Use of model reduction techniques on extremely badly conditioned models (2500 states to 10)
• Use of data driven (hybrid) models
Peter Van Overschee & Christiaan Moons
Slide 42
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
MPC Challenges (3) Linear MPC
• Origin of “Linear” MPC lies in plants running in one operating point (refineries, large crackers)
• Final challenge is the solution of large scale constrained QP problems
• 30 MVs, 30 CVs
• Parameterisation of freedom per MV
• Use of structure in QP problems
• Needs to be solved in limited and predictable time
Peter Van Overschee & Christiaan Moons
Slide 43
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected] Challenges (3)
Non-Linear MPC• New application areas of MPC are:
• Transition Control (broad operating regions)
• Batch Control
• Need MPC valid over a non-linear region of the plant:
• Multiple linear models (more tests)
• Non linear explicit models with fast integration time
• Bounded time for non-linear optimisation part of the MPC
• Convergence and stability ?
• Simple hybrid models ?
Peter Van Overschee & Christiaan Moons
Slide 44
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected] Based Control &
OptimizationDynamic Optimization
Technology Product
Dynamic model based trajectory optimisation
PathFinder
High Performance MPCTrajectory tracking MPC
INCA
Soft Sensors Presto
Optimal Primary PID Controllers
RaPID
ProcessProcess
Process
Model Predictive
Control
Optimal Reference Signals
Model Predictive
Control
Model Predictive
Control
Plant-Wide Model Based
OptimizerOptimal Process Conditions
DCSDCS
DCS
Primary Control Signals
Control hierarchy
Peter Van Overschee & Christiaan Moons
Slide 45
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
Plant-Wide Dynamic Optimisation
Planning/Scheduling
INCAModeler
Process Identification
INCAEngine
LinearModelsLinearModels
Model Predictive Control
Primary Process Control and Instrumentation Systems (DCS’s, PLC’s, etc)
(Simulated) Process (gPROMS, ...)
Process Simulator gPROMS, SpeedUp, ...Rigorous Dynamic
Process Model
RaPID
Soft sensor
Presto
Dynamic OptimizationTrajectory Generation
PathFinder
Peter Van Overschee & Christiaan Moons
Slide 46
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
Plant-Wide Economic Dynamic Optimization Principles
‘Find dynamic MV’s such that objective is optimized
subject to process operation constraints’
Trajectory Optimizer
gPROMS, SpeedUp, ACM,…
Process Model
MV CV
ObjectiveCalculation
Peter Van Overschee & Christiaan Moons
Slide 47
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
Dynamic Optimization Economics
Gasphase Polyethylene reactor
Density--
MI++
Production++
Peter Van Overschee & Christiaan Moons
Slide 48
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
0 5 10 15 20 25 30 35 40 45 50900
950D
ensi
ty
0 5 10 15 20 25 30 35 40 45 503
4
5
LNM
I
0 5 10 15 20 25 30 35 40 45 502k
4k
6k
Pro
duct
ion
0 5 10 15 20 25 30 35 40 45 500
5
10x 10
4
Coo
ling
wat
er12 hours
25 hours
15.000 €/gradechange
No optimization
PathFinder
Dynamic Optimization Economics
Peter Van Overschee & Christiaan Moons
Slide 49
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected] Optimization
Economics Benefits for polymers
• Extension of the production capacity by exploiting the capabilities of the process and pushing towards bottle-neck
constraintsRange: up to 2.5%
• Minimizing operating costs by exploiting the operation freedom
Range: up to 2.5% • Reduce production losses related to grade
transitions • Faster transition policy• Faster settling in the new grade specifications
Range: up to 20.000 Euro/gradechange
• Minimize off-spec production during normal operation
Range: up to 500.000 Euro/year
Peter Van Overschee & Christiaan Moons
Slide 50
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected] Optimization Challenges (1)
Economically Optimal Dynamic Transitions
Smoothly Non Linear Long Calculation Time
Highly Non Linear Short Calculation Time
gPROMS, ACM,
SpeedUp,…
Economic Objective
Mass flow
QualityMV’s ObjectiveConstraints
Economic ObjectiveProcess Model
PricesSpecifications
• Need fast way to do optimise this specific mixed problem with a minimum number of iterations over the Process Model
• Optimal parameterisation of the MV space
Peter Van Overschee & Christiaan Moons
Slide 51
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected] Optim Challenges (2) State of
the Art
y+-
+
+
MPC INCA®
u
Optimal Trajectory Recipe
PathFinder
yu Latest Process Model
Off-Line
On-Line
yopt
uProcess
uopt
y
Extended Kalman Filter
On line model
Peter Van Overschee & Christiaan Moons
Slide 52
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
Dynamic Optimization Challenges (2)
Optimization Society
• On-line Dynamic Optimization: Why not use the whole first principle model to optimize and control the plant
• Need fast and reliably convergent algorithms to solve the on-line optimisation problem
• Need reliable on-line observer algorithms that allow tracking of the model when the plant drifts
• Need fast computers… future
Peter Van Overschee & Christiaan Moons
Slide 53
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
Conclusions
Peter Van Overschee & Christiaan Moons
Slide 54
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
Conclusions • Industrial APC projects are economically driven
• Industrially relevant challenges for identification and control:
• Need algorithms that minimise engineering time
• Need algorithms that allow for simple interaction: high level of automation, easy to use, easy to configure, minimal knowledge required to operate, robust, fast
• Models are expensive ! Need algorithms that reduce testing time and increasing model accuracy
• Need algorithms that allow formulation of the identification and control problems as close as possible to the operational and economic reality
• Need algorithms that can make use of all a-priori knowledge available (physical models and engineering insight)
• Need engineers with Process Knowledge full algorithmic abstraction is a
myth !
Peter Van Overschee & Christiaan Moons
Slide 55
Creators in control
Technologielaan 11/0101, B-3001 Leuven, Belgium T: +32 16 39 30 87, F: +32 16 39 30 80, E:[email protected]
Industrial Challenges for
the Identification and Control
SocietyThank you for your attention!